As cloud computing becomes prevalent, more and more sensitive information are being centralized into the cloud. A basic methodology that may address cloud data privacy issue is to encrypt the data before outsourcing. However, this makes effective data utilization, for example, searching a very challenging task. Although some searchable encryption schemes have been proposed to allow a user to search over encrypted data, these techniques are extremely difficult to provide efficient encrypted data query with various service patterns such as nonuniform data distribution, nonuniform query workload, and attributes join query. In this paper, we research efficient privacy-preserved data query methodologies for querying cipher-text numeric relational data in cloud computing. To provide efficient relational data query service just as DBMS does through SQL, we propose a service-oriented query (SOQ) algorithm that adaptively adjusts the encrypted data buckets based on sensitive data distribution and query workload. Moreover, we propose a two-stage index to address the issue of join query between encrypted attributes that has not been well solved to our knowledge. We design experiments to evaluate performances of our schemes and algorithms, which show that our methods achieve satisfactory encrypted data query performances.
Abstract.Users are motivated to outsource their data into the cloud for its great flexibility and economic saving. However, outsourcingdata to cloud also increases the risk of privacy leak.A straightforward method to protect the users'privacy is to encrypt the files before outsourcing.The existing group key management methods always presume that the server is trustworthy, but cloud storage applications do not meet this condition. Therefore, how to manage the group key to enable authenticated usersto access the files securely and efficientlyis still a challenging problem.In our paper, wepropose a Time-basedGroup Key Management (TGKM)algorithmforcryptographiccloud storage applications, which uses the proxy re-encryption algorithm to transfermajorcomputingtask of the group key management to the cloud server.So, the proposed TGKM scheme greatly reduces the user's computation and storage overhead and makes full use of cloud server to achieve an efficient group key management for the cryptographic cloud storage applications.Moreover, we introduce a key seed mechanism to generate a time-based dynamic group key which effectively strengthens the cloud data security. Our security analysis and performance evaluations both show that the proposed TGKM scheme is a secure and efficient group key management protocol for the cloud storage applications with low overheads of computation and communication.
The current privacy-preserving researches focus on single-owner privacy data. However, multi-owner privacy data, which is also a widespread privacy data, need to be properly protected. At first, the characteristics of multi-owner privacy data and its protection requirement is introduced in this paper. Secondly, a data schema based on deputy mechanism for multi-owner privacy data is proposed. Thirdly, based on the schema, this paper proposes a privacy policy conflict detection method based on sub-graph isomorphic. This method models the privacy policy and each possible policy conflict pattern as a stratified-directed graph (SDG), and provides an algorithm to detect whether the SDG of a privacy conflict mode is isomorphic to that of privacy policies.Y. Ren ( ) The State-Key
the resource distribution in P2P network has an obvious scale free character. Using this inherent character to design resource search strategy is great significant for improving searching efficiency and reducing the costs. We analyze the scale free distribution character in P2P network, and propose a reliable random walk search algorithm to achieve high and reliable search efficiency through forwarding query messages based on the P2P scale free distribution. Moreover, we design simulation experiments to evaluate the performance of reliable random walk. The experimental results show that the reliable random walk is a scalable resource searching algorithm with high search efficiency and low costs.
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